library(ggplot2)
library(readr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(zoo)
## Warning: package 'zoo' was built under R version 4.1.3
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(reshape2)
library(plotly)
## Warning: package 'plotly' was built under R version 4.1.3
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
hotel_bookings <- read_csv("hotel_bookings.csv")
## Rows: 119390 Columns: 32
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (13): hotel, arrival_date_month, meal, country, market_segment, distrib...
## dbl (18): is_canceled, lead_time, arrival_date_year, arrival_date_week_numb...
## date (1): reservation_status_date
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(hotel_bookings)
summary(hotel_bookings)
## hotel is_canceled lead_time arrival_date_year
## Length:119390 Min. :0.0000 Min. : 0 Min. :2015
## Class :character 1st Qu.:0.0000 1st Qu.: 18 1st Qu.:2016
## Mode :character Median :0.0000 Median : 69 Median :2016
## Mean :0.3704 Mean :104 Mean :2016
## 3rd Qu.:1.0000 3rd Qu.:160 3rd Qu.:2017
## Max. :1.0000 Max. :737 Max. :2017
##
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:119390 Min. : 1.00 Min. : 1.0
## Class :character 1st Qu.:16.00 1st Qu.: 8.0
## Mode :character Median :28.00 Median :16.0
## Mean :27.17 Mean :15.8
## 3rd Qu.:38.00 3rd Qu.:23.0
## Max. :53.00 Max. :31.0
##
## stays_in_weekend_nights stays_in_week_nights adults
## Min. : 0.0000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 1.0 1st Qu.: 2.000
## Median : 1.0000 Median : 2.0 Median : 2.000
## Mean : 0.9276 Mean : 2.5 Mean : 1.856
## 3rd Qu.: 2.0000 3rd Qu.: 3.0 3rd Qu.: 2.000
## Max. :19.0000 Max. :50.0 Max. :55.000
##
## children babies meal country
## Min. : 0.0000 Min. : 0.000000 Length:119390 Length:119390
## 1st Qu.: 0.0000 1st Qu.: 0.000000 Class :character Class :character
## Median : 0.0000 Median : 0.000000 Mode :character Mode :character
## Mean : 0.1039 Mean : 0.007949
## 3rd Qu.: 0.0000 3rd Qu.: 0.000000
## Max. :10.0000 Max. :10.000000
## NA's :4
## market_segment distribution_channel is_repeated_guest
## Length:119390 Length:119390 Min. :0.00000
## Class :character Class :character 1st Qu.:0.00000
## Mode :character Mode :character Median :0.00000
## Mean :0.03191
## 3rd Qu.:0.00000
## Max. :1.00000
##
## previous_cancellations previous_bookings_not_canceled reserved_room_type
## Min. : 0.00000 Min. : 0.0000 Length:119390
## 1st Qu.: 0.00000 1st Qu.: 0.0000 Class :character
## Median : 0.00000 Median : 0.0000 Mode :character
## Mean : 0.08712 Mean : 0.1371
## 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :26.00000 Max. :72.0000
##
## assigned_room_type booking_changes deposit_type agent
## Length:119390 Min. : 0.0000 Length:119390 Length:119390
## Class :character 1st Qu.: 0.0000 Class :character Class :character
## Mode :character Median : 0.0000 Mode :character Mode :character
## Mean : 0.2211
## 3rd Qu.: 0.0000
## Max. :21.0000
##
## company days_in_waiting_list customer_type adr
## Length:119390 Min. : 0.000 Length:119390 Min. : -6.38
## Class :character 1st Qu.: 0.000 Class :character 1st Qu.: 69.29
## Mode :character Median : 0.000 Mode :character Median : 94.58
## Mean : 2.321 Mean : 101.83
## 3rd Qu.: 0.000 3rd Qu.: 126.00
## Max. :391.000 Max. :5400.00
##
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:119390
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06252 Mean :0.5714
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
##
## reservation_status_date
## Min. :2014-10-17
## 1st Qu.:2016-02-01
## Median :2016-08-07
## Mean :2016-07-30
## 3rd Qu.:2017-02-08
## Max. :2017-09-14
##
count_df = as.data.frame(table(hotel_bookings$is_canceled))
head(count_df)
## Var1 Freq
## 1 0 75166
## 2 1 44224
ggplot(count_df, aes(x= c("Non-canceled", "canceled"), y = Freq))+
geom_bar(stat="identity", position=position_dodge(width=0.8), width = 0.7, fill = "lightblue")+
geom_text(aes(label = Freq), vjust = 1.5)+
xlab("")+
ylab("Count")+
ggtitle("Total count of canceled and non canceled hotel bookings")+
theme(text=element_text(size=14, family="Times"))
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count_df = as.data.frame(table(hotel_bookings$is_canceled, hotel_bookings$hotel))
count_df$Var1 = as.character(count_df$Var1)
count_df[count_df == "0"] = "Non-canceled"
count_df[count_df == "1"] = "canceled"
head(count_df)
## Var1 Var2 Freq
## 1 Non-canceled City Hotel 46228
## 2 canceled City Hotel 33102
## 3 Non-canceled Resort Hotel 28938
## 4 canceled Resort Hotel 11122
colnames(count_df)[2] = "Hotel_type"
ggplot(count_df, aes(x= Var1, y = Freq, fill = Hotel_type))+
geom_bar(stat="identity", position= "dodge", width = 0.7)+
# geom_text(aes(label = Freq), vjust = 1.5, hjust = 3)+
xlab("Reservation")+
ylab("Count")+
ggtitle("Total count of canceled and non canceled hotel bookings")+
theme(text=element_text(size=14, family="Times"))
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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non_cancelled_res = subset(hotel_bookings, hotel_bookings$is_canceled == 0)
non_cancelled_res$guests = non_cancelled_res$adults+non_cancelled_res$children + non_cancelled_res$babies
mapdf = data.frame(
non_cancelled_res$arrival_date_month, non_cancelled_res$hotel, non_cancelled_res$arrival_date_year, non_cancelled_res$guests
)
colnames(mapdf) = c("arrival_date_month", "hotel", "arrival_date_year", "guests")
mapdf = subset(mapdf, mapdf$guests != 0)
#View(mapdf)
heatmapdf = mapdf %>%
group_by(arrival_date_month, arrival_date_year, hotel) %>%
summarise(people = sum(as.numeric(guests)))
## `summarise()` has grouped output by 'arrival_date_month', 'arrival_date_year'. You can override using the `.groups` argument.
head(heatmapdf)
## # A tibble: 6 x 4
## # Groups: arrival_date_month, arrival_date_year [3]
## arrival_date_month arrival_date_year hotel people
## <chr> <dbl> <chr> <dbl>
## 1 April 2016 City Hotel 4035
## 2 April 2016 Resort Hotel 2410
## 3 April 2017 City Hotel 4111
## 4 April 2017 Resort Hotel 2277
## 5 August 2015 City Hotel 2451
## 6 August 2015 Resort Hotel 2344
HEAT MAP FOR the GUEST BOOKINGS OVER TIME.
ggplot(heatmapdf, aes(x = factor(arrival_date_month, levels=c("January", "February", "March","April", "May", "June", "July", "August", "September", "October", "November", "December")), y = factor(arrival_date_year, levels = c(2017,2016,2015)), fill = people))+
geom_tile()+
scale_fill_distiller(palette = "Magma", direction=1)+
geom_text(aes(label = people), size = 3)+
#scale_x_discrete(limits = c("Jan", "Feb", "Mar","Apr", "May", "Jun", "Sep", "Oct", "Nov", "Dec"))+
facet_grid(hotel ~ .)+
xlab("Month")+
ylab("Year")+
ggtitle("Heatmap of guests")+
theme(text=element_text(size=14, family="Times"),axis.text.x = element_text(angle = 30, hjust = 1))
## Warning in pal_name(palette, type): Unknown palette Magma
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ROOM COST VARIATION IN HOTELS.
Roomdf = data.frame(
non_cancelled_res$arrival_date_month, non_cancelled_res$hotel, non_cancelled_res$adr, non_cancelled_res$guests
)
colnames(Roomdf) = c("arrival_date_month", "Hotel", "adr", "guests")
Roomdf = subset(Roomdf, Roomdf$adr > 0)
#View(Roomdf)
df = Roomdf %>%
group_by(Hotel, arrival_date_month) %>%
summarise(price = sum(as.numeric(adr))/sum(as.numeric(guests)))
## `summarise()` has grouped output by 'Hotel'. You can override using the `.groups` argument.
head(df)
## # A tibble: 6 x 3
## # Groups: Hotel [1]
## Hotel arrival_date_month price
## <chr> <chr> <dbl>
## 1 City Hotel April 55.7
## 2 City Hotel August 55.5
## 3 City Hotel December 46.3
## 4 City Hotel February 47.0
## 5 City Hotel January 47.2
## 6 City Hotel July 54.6
ggplot(df, aes(x = factor(arrival_date_month, levels=c("January", "February", "March","April", "May", "June", "July", "August", "September", "October", "November", "December")), y = price, fill = Hotel))+
geom_bar(stat="identity", position= "dodge", width = 0.7)+
#geom_text(aes(label = price), vjust = 1.5, hjust = 3)+
xlab("Month")+
ylab("Price Per Guest")+
ggtitle("Room cost per guest based on month and hotel")+
theme(text=element_text(size=14, family="Times"),axis.text.x = element_text(angle = 30, hjust = 1))
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Room Cancellations
RoomCandf = as.data.frame(table(hotel_bookings$is_canceled, hotel_bookings$hotel, hotel_bookings$arrival_date_month))
RoomCandf = subset(RoomCandf, RoomCandf$Var1 == 1)
colnames(RoomCandf)[1:3] = c("is_canceled","Hotel","arrival_date_month")
#View(RoomCandf)
ggplot(RoomCandf, aes(x = factor(arrival_date_month, levels=c("January", "February", "March","April", "May", "June", "July", "August", "September", "October", "November", "December")), y = Freq, fill = Hotel))+
geom_bar(stat="identity", position= "dodge", width = 0.7)+
#geom_text(aes(label = price), vjust = 1.5, hjust = 3)+
xlab("Month")+
ylab("Canceled count")+
ggtitle("Canceletion count based on month and hotel type")+
theme(text=element_text(size=14, family="Times"),axis.text.x = element_text(angle = 30, hjust = 1))
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Tree Diagram
library(treemap)
## Warning: package 'treemap' was built under R version 4.1.3
library(treemapify)
## Warning: package 'treemapify' was built under R version 4.1.3
Roomcostdf = data.frame(
non_cancelled_res$hotel, non_cancelled_res$adr, non_cancelled_res$guests, non_cancelled_res$reserved_room_type
)
colnames(Roomcostdf) = c("Hotel", "adr", "guests", "reserved_room_type")
Roomcostdf = subset(Roomcostdf, Roomdf$adr > 0)
#View(Roomdf)
df = Roomcostdf %>%
group_by(Hotel, reserved_room_type) %>%
summarise(price_of_reserved_room_type = sum(as.numeric(adr))/sum(as.numeric(guests)))
## `summarise()` has grouped output by 'Hotel'. You can override using the `.groups` argument.
df1 = subset(df, df$Hotel == "City Hotel")
fig1 <- plot_ly(
type="treemap",
labels=df1$reserved_room_type,
parents = c("City Hotel","City Hotel","City Hotel","City Hotel","City Hotel","City Hotel","City Hotel"),
values = df1$price_of_reserved_room_type,
marker=list(colorscale='Reds'),
textinfo="label+value")
fig1
df2 = subset(df, df$Hotel != "City Hotel")
fig2 <- plot_ly(
type="treemap",
labels=df2$reserved_room_type,
parents = c("Resort Hotel","Resort Hotel","Resort Hotel","Resort Hotel","Resort Hotel","Resort Hotel","Resort Hotel","Resort Hotel","Resort Hotel"),
values = df2$price_of_reserved_room_type,
marker=list(colorscale='Reds'),
textinfo="label+value")
fig2
Meal Price per guest
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble 3.1.6 v stringr 1.4.0
## v tidyr 1.2.0 v forcats 0.5.1
## v purrr 0.3.4
## Warning: package 'tidyr' was built under R version 4.1.3
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x lubridate::date() masks base::date()
## x plotly::filter() masks dplyr::filter(), stats::filter()
## x lubridate::intersect() masks base::intersect()
## x dplyr::lag() masks stats::lag()
## x lubridate::setdiff() masks base::setdiff()
## x lubridate::union() masks base::union()
library(hrbrthemes)
## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(viridis)
## Loading required package: viridisLite
Mealdf = data.frame(
non_cancelled_res$meal, non_cancelled_res$hotel, non_cancelled_res$adr, non_cancelled_res$guests
)
colnames(Mealdf) = c("meal", "Hotel", "adr", "guests")
Mealdf = subset(Mealdf, Mealdf$adr > 0)
boxdf = Mealdf
#View(Mealdf)
df = Mealdf %>%
group_by(Hotel, meal) %>%
summarise(price = sum(as.numeric(adr))/sum(as.numeric(guests)))
## `summarise()` has grouped output by 'Hotel'. You can override using the `.groups` argument.
df = subset(df, df$meal !="Undefined")
#View(df)
ggplot(df, aes(x = meal, y = price, fill = Hotel))+
geom_bar(stat="identity", position= "dodge", width = 0.7)+
xlab("Meal Type")+
ylab("Price Per Person")+
ggtitle("Meal Type cost per person")+
theme(text=element_text(size=14, family="Times"),axis.text.x = element_text(angle = 30, hjust = 1))
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boxdf = subset(boxdf, boxdf$meal !="Undefined")
boxdf$price = boxdf$adr/boxdf$guests
boxplot(price ~meal*Hotel,data=boxdf,col=(c("gold","darkgreen")), main="Meal Type cost per person",
xlab="Meal Type", ylab="Price Per Person")
## Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group
## == : Outlier (Inf) in boxplot 1 is not drawn
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ggplot(boxdf, aes(x = meal, y = price, fill = Hotel))+
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
xlab("Meal Type")+
ylab("Price Per Person")+
ggtitle("Meal Type cost per person")+
theme(text=element_text(size=14, family="Times"))
## Warning: Removed 31 rows containing non-finite values (stat_ydensity).
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